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    A.I. & Machine Learning: Investing in Tech on Wall Street (w/ Hari Krishnan & Vasant Dhar)


    HARI KRISHNAN: My name is Hari Krishnan I'm a fund manager at Doherty Advisors, and I've been on this station before talking about ETFs and dangers in the VIX and the VIX markets

    It's a pleasure to introduce Vasant Dhar, who is a founder of SCT Capital, one of the first machine learning hedge funds in existence, a professor at the Stern School of Business at NYU, and a director of the PhD program in the Center for Data Science, also at NYU It would be interesting for me at least to know how you got started in machine learning, and how you got started in finance, two apparently disparate areas at least back in the day VASANT DHAR: Yeah Well, great to b, Hari Great to be having this conversation

    Strangely enough, I got into machine learning because of Nielsen, the media company They have a household division and they were tracking lots of households and what they were purchasing and they gave me this data and said, "Can you find some interesting patterns in it?" This is like 1990 I said, "To what end?" And they said to me, "We'd like to do how new products, we'd like to know how new products do, what products do well, what their selling patterns are" It went off for a few weeks, and we had a meeting and they said, "Vasant, what'd you find?" I said, "I found lots of things, but I can't explain them Such as a lot of older women in the northeast shop on Thursdays

    " They said, "Oh, yeah, that's coupon day What else did you find?" I was really excited because I hadn't actually told the machine to look for any such thing but there were reasons behind the patterns that were found That just led to more interesting stuff in the data I became a believer in these machine learning methods, because they seem to be finding interesting stuff Then fast forward three years, I was introduced to a gentleman called Kevin Parker who'd been hired, who'd been appointed by John Mack who's running Morgan Stanley at the time to run tech and Kevin was a big believer in technology, and he brought me in to implement machine learning at Morgan Stanley

    I think I brought machine learning to Wall Street in the mid-90s The two problems we were looking at was customer data, and then financial market prediction I did both of those things and gravitated towards the market prediction side of things If you know anything about proprietary trading groups, they want to know everything you know but they don't want to tell you anything they know I proposed a simple experiment to them

    I said, "Just give me all the trades you've done in the last few years and I'll tell you if you could have done better," and they said, "You don't need to know anything about the strategy" I was like, "Nope" I took the trades I did some Hocus Pocus, let's call it, but I literally amended the trades with market state information and I cranked this generic rule learning algorithm I've been working on at that time, the tools were far and few between and you had to build your own It came up with these patterns

    I remember I went to the first trading meeting It was the same dèjà vu all over again, Kevin saying, "Vasant, what'd you find?" I said, "I found a bunch of things, but I'm not sure what they mean" "It's all right, take it from the top" I said, "Well, when the 30-day volatility is in the last quartile, your trades are three times as profitable as they are otherwise" There was silence around the room for a little while and then they tried to digest the implications of it and started talking to each other

    I was just watching this and I said, "Can someone tell me what's going on?" They said, "Not really, but we've felt that we lose a lot of money when volatility spikes It's interesting that you're telling us that volatility actually matters" Now, the interesting thing about that incident was that I only learned the reasons for why I discovered the pattern much later That was the first lesson, which is that when you have this data driven approach to life, you find that patterns often emerge before the reasons for them, because– HARI KRISHNAN: Let me ask a quick question there Which is, if let's say, I've been trading for 20 years, am I better off bringing in a machine learning expert who's never traded, who might find some unbiased, as you might put them, structure in the data than bringing somebody in who actually knows a lot about finance, and might have certain prior expectations that might be valid, might push them in a certain direction? VASANT DHAR: Great question

    Now, remember, the space I got started was I didn't actually come up with the strategy The strategy already existed, which is what you're saying, you've been trading for 20 years, you've got a strategy and you bring someone in Now, when you bring someone in, they're going to analyze your strategy, but they're not going to actually develop it They'll analyze it and they'll tell you if you could have improved it That was what I really did

    It was an easier problem that I started with than let's say building my own strategy, which was the next step What they said was, "Hey, that's interesting Do you think you can get the machine to discover new strategies?" I said, "Sure, in principle, that should be possible" That's what led me down this path to where I am since that time It was initially an improvement, an overlay on an existing strategy, but I need to know anything, but I needed to know machine learning to do that and I needed to apply the method, the scientific method correctly, but I didn't have to do anything creative

    The machine did the heavy lifting for me after I told it what I thought might actually distinguish good trades from bad trades, it could then find that for me, but I did very little I just told it well, consider volatility, consider trend, consider stochastics, like the usual thing and found that for me, but it's a completely different ballgame when you don't have that and you have to start from scratch and get the machine to actually discover these strategies for you That area is much more treacherous and you have to be really careful in how you do that HARI KRISHNAN: Got it I know obviously, there's some famous unnamable hedge funds that do focus on hiring people who don't have experience

    I presume, as you said, that they're simply trying to improve existing processes, models and trading systems instead of trying to build something from the ground up VASANT DHAR: Correct HARI KRISHNAN: Well, that's a very important point Now, I occasionally dip into the internet and Google search this and that and the other one I'm going to have some time to kill and I see that everyone wants to hire a machine learning graduate PhD expert and so on If I were sitting on the other side of the table, which I am occasionally, I would be scratching my head saying, "How do I know what's real and what's fake?" Fake is a strong word, because there's always some level of confidence, some probability

    What's your vibe? What's your take on this whole question? VASANT DHAR: Yeah, that's the central question in machine learning, where should you trust what it's telling you? What's often overlooked about machine learning is that as a problem gets harder to predict or as it gets noisier– I look at the world in terms of predictability spectrum, completely random to completely predictable so all problems lie on this As you move towards the randomness end of the spectrum, your models can become very unstable What that really means is that if you change your training set, the data on which you're going to build the model slightly, the model is generated by the machine can actually change quite dramatically If that happens, you really shouldn't trust the machine, you should not trust that model If you get like a high variance in what's generated, and we'll come back to what variance really means, but if you get this high variance in what the machine is generating, you shouldn't trust it

    That's the core question that we focus on is, I focus on is when should you trust the machine? When should you trust the outputs of a machine? My very simple answer to it is when there's stability When there's a stability in the outputs, and so when you get to that point, you can say, "All right, I think I've– but that's a necessary condition, but not a sufficient one" You need stability, to have some confidence that I'm not going to get a completely different set of trading decisions if I changed my training data slightly That should give you cause for discomfort HARI KRISHNAN: God, one more primitive way to think about it is to say, well, stability must be related to some information criteria

    I don't want to get too fancy, but if I have a really simple model, and it works, maybe it's automatically more stable Where do you beat that? VASANT DHAR: Yeah, exactly You've gotten to the heart of it, which is I said, it's necessary but not sufficient The simplest model could be, you always bet the average That's a simple model

    It has zero variance You will always do the same thing It's probably isn't useful It has heavy bias, and it probably isn't very useful You're trying to tease apart the structure in the space into like, good longs, good shorts

    That means that you're introducing some level of complexity now over and above that simple like that the average model You're now introducing a little bit of complexity for more predictability and you might now sacrifice some degree of explainability for that increased predictability that you get from the complexity HARI KRISHNAN: Is that trade off the art of this business or is it something that you can quantify in some way? VASANT DHAR: You always want to quantify something like that How successful you can do it is of course questionable, but it is something one should be able to or at least measure parts of it Complexity for sure, you can specify how complex you want a model to be, how complex you want the machine to be able to– the complexity that you want it to be able to work with

    You can specify that depending on the form of your model, the parameters would vary You should be able to look at analyze the variance of the model Since I've already mentioned variance, let me just sketch it out, like variance has two types, it's the variance in performance of the model If you change the input set slightly, how widely does your output performance vary? That's the– or rather the variance of the performance, how high is that? The other part is your decisions, how do your decisions change as a function of small variations in your training set? Because if your decisions change a lot, that's also indicative of instability even though your performance may not change HARI KRISHNAN: Got it

    VASANT DHAR: Those two elements is what I look at as variance, it's variance in performance and it's the variance in decisions HARI KRISHNAN: I'm going to jump around a little bit and ask another question, which is if I were a viewer of this show, and I wasn't an expert in machine learning and somebody sent me a big bank sell side report showing the performance of a machine learning algo in a given market, let's say currencies or rates, what can I actually do with that? Is that totally useless? VASANT DHAR: Well, the question is, is it real or is it simulated? HARI KRISHNAN: If it only give me the results VASANT DHAR: But is it real? It's actual trading performance, or is it this is what I would have achieved? HARI KRISHNAN: This is what I would have achieved VASANT DHAR: Well, that's very difficult to trust just by looking at it because you really have to peel it apart and understand what was the methodology? How many times did you look at the data? Did you follow a process and I think this is the first time I'm talking about process I'll get to that and I'll explain what I mean by that, but did you follow a standard process in how you generated this set of outputs, as opposed to, well, let's try this Oh, that doesn't work too well, let's try something else and oh, now it looks great

    There's a famous saying that I never saw back test I didn't like How often have you seen a really poor back test being marketed? You don't HARI KRISHNAN: I used to go to a series of talks where every talk– I'm actually cribbing off somebody else, ended with a graph that started at the lower left corner of the page, of the slide, and then wound up at the upper right corner VASANT DHAR: The short answer to that question is I wouldn't trust a back test unless [indiscernible] my own and I know exactly what the assumptions were that went into it and the process that was followed, and one of my goals in life has actually been to get to the point where my back tests and reality are indistinguishable My back test don't look particularly impressive, but I trust that this is what I'll achieve in reality

    By not too impressive, I mean that a back test is a point estimate It says, your expected information ratio is point six, and while we're talking about it, I want to say something about this, which is that I think anyone who's built strategies for some time knows that they realize performance sometimes bears very little resemblance to their back test in the short term, and sometimes even in the long term Your objective should be that they should mirror each other in the long term In the short term, things are there's a lot of noise, things are really unpredictable but in the long term, your back tests and reality should really mirror each other That will be indication that you've got a robust process, and that you have the right set of complexity in your model if you can get to that point, but that's– HARI KRISHNAN: Okay

    That's a good point Now, debunk this idea I knew a guy once and he had a model that traded various equity sectors, wasn't a machine learning model mind you, but then it did various things that would be now commonly known factor modeling this, that and the other Every now and again, he'd have a period of underperformance so he traded 10 sectors, whatever He would gleefully call people up and say, oh, I've improved my system by throwing out the sector that didn't work when the model didn't work

    That seems a bit overly greedy in the language of algorithms What do you find are the dynamics of the algorithms or the systems that you look at? Do you just throw out a model that doesn't work well for a while or do you believe that there is some regime dependence that may make it valid at some point in the future? VASANT DHAR: This question used to drive me crazy before I got to the point where I developed a process for generating my strategies Because I started using machine learning in the '90s but for the first 10 years or so, a little maybe 12 years, the models I created were human curated I'd look at the output of the machine learning algorithm Then I would say, "Well, let me reduce the complexity here

    Let me round it off here, let me do this, make all these changes, and it would be a human curated model" I found that over time, there was generally a degradation in performance We didn't go smoothly down, it would go like that Every time it went like that, I'd think it's working again, and then we'll go like that I just realized, and this is common knowledge in the theory of finance that humans just tend to be exceptionally poor at making decisions about timing and direction and I was no better than that

    It used to it was really a vexing problem where every month I'd have to make a decision as to which models to field and which ones to put away and how much risk to give them? Even though the optimization, it doesn't really make it easy because I had to make these decisions HARI KRISHNAN: That branches in two directions VASANT DHAR: It does, and my initial direction was, well, let's just use a mean variance optimization approach and the models that have been performing poorly will automatically get dropped out because the expectation is low and negative That has its own set of issues, which is that your risk exposure could then swing around like crazy because all your equity models are doing well and your fixed income aren't That has its own set of problems so that was not the solution

    What I finally converged on as a solution was not to use optimization, but actually develop a process That were at regular intervals after you accumulated more data, you keep accumulating data As you accumulate more data, you learn from that additional data and you retrained To me, that was a much more graceful way of dealing with alpha decay because you're saying, "Well, I'm going to follow a process and I realized that the world changes, and I'm just going to keep incorporating more and more of the world, the data into my process As long as I trust my process, I should trust the outputs that emerge from that process

    " That was my way of dealing with this issue of alpha decay, and what do I do What it made me realize is that it's you need two things, you need ideas, but you also need a process It's those two things together that make up a strategy That is without a process, you are groping and probing and exploring, but you don't have a firm criterion for what you should believe, which is what a process gives you HARI KRISHNAN: Well, I don't get worried that much in the markets but what I do, what does worry me particularly is this tendency that we've had since 2012, let's say, of incredibly quiet markets

    On the one hand, I know sounds naive, you've seen a huge increase in the amount of data, you've seen Moore's Law, or some super linear growth in computational power, and so on and yet it could be argued that the data set that's been collected over these years is not all that rich because markets have been artificially in zombie land, whenever it's called, zombification is the word If the regime changed, the machines that are learning gradually as the data comes in, so you can think of things– again to use a fancy phrase and a Baisean mindset where you have an inference engine, it's learning all this stuff, I met Vasant 10 times, the 11th time, he's rude to me or I'm rude to him, let's say, but it doesn't make me revise my opinion of him that quickly because I have this huge aggregation of prior experience Now, if all of these inference engines or learning algos are loading on the past three years, five years, even eight years, who's to say they won't be exposed in the next crisis? How do you account for that? How do you account for breakpoints, discontinuities, and the fact that everyone is probably chasing at some course level, similar traits? VASANT DHAR: You're getting at a really interesting question This is where the human input or the creativity comes into the picture as part of the modeling process What you're really saying is that what we've observed is one realization out of many possible realizations of reality

    We've just observed this one thing, and the one thing we've observed is markets during a period of declining rates Now, what you should really be doing also is looking at markets during periods of rising rates You go to, let's say to 1994, and you go to 1997 and you go to 2004, where you've actually had periods where the Fed was tightening You need to exercise some human oversight, and by the way, this could be also part of your process is that you need to exercise some oversight in terms of how you're going to guide the machine, or how you let the machine do its thing You're right, if you just give it a period of declining rates, it'll just tell you, you should have been long this whole time

    Being long bonds would have been awesome, but that doesn't mean being long bonds going forward will be awesome It may or may not? It's not known Rates go to zero Sure It'll be an awesome trade

    If rates remain where they are, and I think the last four or five years, all the experts have been saying rates would go up, but they haven't They've gone up, but they've gone back down They've remained pretty much where they were like several years ago, give or take It's hard to point to like– HARI KRISHNAN: This year, they've been going down like no tomorrow VASANT DHAR: Right, but occasionally, they've gone up as well, but you're right

    This has been largely, again, declining rates This is what you need to account for when you do your modeling is are you just going to ignore those periods of rising rates? That will probably unwise You do want periods in your training set to the extent possible that do give you that balance of potentially long and short trades HARI KRISHNAN: How do you conceptualize that because one could argue as I put it that the credit cycle is part of life It's part of markets, it can never be erased

    Central banks can try erase it, but that will just simply prolong the cycle and make it more exaggerated and so on One could also argue that in 1994 and 1997, structurally, markets were quite different There was much less impact of computers trading, ETFs hadn't blossomed or polluted the landscape, depending on your view Structurally there, the markets were quite different How would you discount a period which richens your data says, but which is either old or perhaps not so reflective of the current dynamics? VASANT DHAR: Yeah, I don't think you can discount your data set

    I think you have to include as many periods as possible of different types of regimes Now, the question is what do you really mean by a regime and you could say, well, a period of declining rates is a regime, a period of increasing rates is a different regime You need to incorporate different types of regimes into your training data The other question you're raising is a really hard one about structural differences because while you're right that structurally, the factors impacting the market today are completely different from those in the '90s or even 10 years ago For people like us who work with prices, the implicit assumption you're really making is I don't care what the drivers are

    They show up as these indicators of fear and greed or whatever you want to call them, volatility, trend, whatever It's those exogenous factors that are making their presence felt in terms of these things you're measuring, and you're saying, I don't really– I can't get into the causes and analyze the structural properties of these different markets, because then I've got nothing to work with, but then I've got four data points HARI KRISHNAN: That's a key point, you're saying that the fact that you're using ML on price data is significantly different in terms of the richness of your historical data set from someone who might say, well, I use satellite images or I use shipping data, or I use– how would you address that for someone who's thinking about that and isn't sure what the relative importance [indiscernible]? VASANT DHAR: I'm glad you brought that up Because one of the things that you need with machine learning methods is lots of data If your data frequency– the lower the frequency, the less you're able to learn, the less faith you have in something that you come up with because you've only seen so many instances of something

    Using monthly data, it's fine using monthly data, but there's a limited amount of action you can get with that because you just don't have enough instances The denser your data set becomes, the better off you are, which is why you do so much better at shorter trading frequencies, like higher frequency trading is already machine based Humans don't stand a chance there That's already been taken over by machines for good reason Longer term investing, there's no scope for machine learning, because there's not enough data there

    That Warren Buffett investing with periods of holding periods of years, but you don't have enough data for that The real sweet spot for machine learning is in the denser parts of the price space, which is intraday daily data When you start going way less frequently than daily, it just starts becoming problematic You just don't have enough data to learn anything from reliably HARI KRISHNAN: Well, but let's try an analogy that confuses me that maybe contradicts this, but maybe it doesn't, which is think of credit card transactions

    Maybe one out of 10,000 or 20,000 transactions are fraudulent so the instances where you actually have a fraudulent transaction are very low, therefore you can get a great prediction in terms of accuracy by just saying every transaction is valid You're dealing with a very thin data set, even though there's a lot of time, there's lots of transactions, you're dealing with stuff that doesn't happen that often within that data set Is that an issue for you as well? For example, let's say the markets were going up in a straight line, bonds were going up in a straight line Is it hard to develop a bond shorting or an equity shorting machine learning algo? VASANT DHAR: Not for that reason The case that you're bringing up is something occurs very infrequently, so the base rate

    That's what it's called The base rate is very low, but the base rate is 1% or a 10th of a percent, but that's the class you're interested in predicting You're not interested in predicting nonfraud, you're just interested in predicting fraud To say that my model is 99% accurate, if I just predict everything is non fraud is useless You're bringing up an interesting point, which is that predictive accuracy is not what you're aiming for when it comes to the minority class, when it comes to predicting things that are infrequent

    I didn't finish answering your previous question, which is– HARI KRISHNAN: Keep rolling on that way VASANT DHAR: Why is it so hard to get good shorts with, let's say, the S&P or bonds? That's not because the base rate is low which is, let's say fraudulent transactions are– they happen 1% or 1/10th of the percent of the time, whereas the S&P 500 actually goes down 46% or 47% of the time You actually do have a fair amount of days where the market goes down, but it's still hard to make that prediction because the problem is so noisy, that the machine just has this tendency to amplify its long bias That's the reason why it's hard Because the problem is noisy

    There's very little signal Let's say the extreme case The extreme case would be there's no signal in the problem You should always be long Your– there's a premium so your output will be I'm always long even though the actual distribution is quite wide

    HARI KRISHNAN: People are behaving very rationally nowadays by that metric They have no signal VASANT DHAR: Correct If there's no signal then they'll just bet the mean, but as you get more signal into the problem, your predicted distribution begins to mirror the actual distribution In the case of complete perfect signal, your predictions are exactly the same as the actual and your two distributions look alike in that extreme case, on the complete predictability side

    On the completely random case, your predicted distribution becomes aligned if you always make the same prediction Think of anything else in between as a situation where your predicted distribution is much narrower than the actual and that's because of the noise in the problem The machine is not going to stick its head out and say, tomorrow's going to be a big update, because chances are it'll be wrong or tomorrow's going to be big down day, chancer are it'll be wrong HARI KRISHNAN: You're saying that formulating the problem in a Nassim Taleb sense doesn't really fly In other words– VASANT DHAR: No, it doesn't

    HARI KRISHNAN: Try, instead of saying that your minority clause is the S&P goes down one day, saying that it goes down by two or three standard deviations VASANT DHAR: Now, you predict the magnitude is important HARI KRISHNAN: Got it Those are pretty rare VASANT DHAR: Those are pretty rare, which is why you don't predict them

    The machine is going to predict that tomorrow is going to be down 3% day because chances are it will be wrong, but it will predict the mean HARI KRISHNAN: Okay Well, I'll ask a simple minded question there then, which is that we've seen that systematic traders, not machine learning based traders, but systematic traders had a real problem in August of 2007 There was a quant crisis, the equity long short guys, now, they had the freedom, the ones who did relatively well, in some cases, had the freedom to turn one knob which said I'm getting out My model is not working

    I'm not a human intervention trader, but I can shut the thing off Is that consistent with your view, or is it–? VASANT DHAR: You always have to be able to do that HARI KRISHNAN: You have to have that option VASANT DHAR: You have to have that option When Fukushima happened, we stopped trading the Nikkei and the JGB

    It was like we have no idea what's going to happen in Japan, just turn those models off Acts of God, rare events where you know that the risks are elevated and this is a complete crapshoot that there's no way you could have learned anything useful that will apply in that scenario, you may get lucky, you may get unlucky, chances are you should just turn it off and get out and until [indiscernible] returns, which is not easy to determine but that will be humanly the right thing to do HARI KRISHNAN: I recall reading somewhere that George Soros used to his back would hurt if he was losing too much money and he didn't know why so he cut his possessions I don't know if that's true or just fantasy on my part Is that the analog here where you don't want humans to intervene with everything or even much of everything but at some point, you should have the right to just reduce exposure, cut your losses, analyze everything and then get back into the system

    VASANT DHAR: You should and I tell my investors that as well we are systematic and we always follow the machine, but there are situations that can happen every few years These things shouldn't be happening every few days a month, because that means you're interfering too much On average, we've had a few cases, the Fukushima was one, the taper tantrum was another one where it just seemed that this is something new You have to make that determination, but it's not easy and that is something that we'll occasionally talk about and say, does this qualify as something just completely different? You want to most of the time be able to say no, but every once in a while, something is different and you should have the right to just turn it off HARI KRISHNAN: What constitutes something being different

    Now let's say that stocks and bonds started going down together Is that different enough? VASANT DHAR: No, that's just something– that could be just liquidation Just people just getting out and there's massive liquidation going on You could say that this seems like unusually heavy liquidation I don't want to take the risk

    Now, how do I define that criterion? I don't know If I could then it would have been part of my algorithm That's one of those things where you say I don't know I haven't enumerated all possible states of the world of possibilities here As a human being, presumably you have the intelligence to recognize one of these when it occurs

    HARI KRISHNAN: There's a crude analogy that machines don't feel emotion so I would never trade– I might trade badly, but I'd never trade a million contracts instead of 1000 Whereas a machine barring the appropriate filter, which naturally, in all likelihood would be in place, wouldn't see a difference There's some– in extremes, humans have some override capability that perhaps machines dealt Is that what we're agreeing upon here, or is that too simplistic? VASANT DHAR: No, I think that's fair A machine by itself will only turn itself off if you actually told it, like if the VIX goes above 40, just turn off

    You could specify that as a rule and say about that, to me, that means that the world is weird The VIX is above 40, or you can define those things There are other things where you say, I don't know, I'll just have to wait and see because the future is always new It's never exactly like the past even though it might rhyme with it, as you once noted, but if it's sufficiently different, and it's just something that's weird and looks treacherous, then you have to make that decision to turn it off Nothing is always 100% systematic

    HARI KRISHNAN: Now, before I get too depressing about this, you've been doing this for many years, well over 10 years running your AQT system, if someone wanted to do the same thing today, and they had a decent amount of money bankrolling the thing, but they had no experience, what barrier to entry would they face? What things have you learned that they wouldn't know? They'd have to learn the hard way, that significantly make a system of run by somebody like you more robust? VASANT DHAR: It boils down to experience HARI KRISHNAN: What specifically have you learned? VASANT DHAR: Making mistakes It's important to make mistakes HARI KRISHNAN: Now, any specific kind, sizing positions? VASANT DHAR: Where do I start? This could be a really long conversation if I get into all the mistakes I've made, which probably would not be a good thing Yeah, you learn through your mistakes

    In a domain like this, there's you can make lots of mistakes because it's treacherous It's a noisy problem There's a famous Richard Fineman quote, the easiest person to fool is yourself so you have to be really careful to not fool yourself There is a tendency for us to want to believe in things that something works That can be dangerous, because you want to believe something, we desperately want to believe that stuff works

    All of every bone in your body should be telling you to figure out what's wrong with something that seems to work, because most things should not work If you see some stuff that works, you really ought to question it 10 times as hard because chances are, there's something wrong there That's been a great learning experience for me to get to the point where you believe something works and you find there's a problem with that The problem can be a methodological one Maybe there was part of your process that was flawed, maybe you aren't taking care of outliers properly, maybe your complexity level is too high or maybe you're not just taking some reality of the market into account

    It's a combination of just experience in terms of market knowledge, and just seeing how markets behave, because unless you feel the pain and the pleasure from actually trading, it's all hypothetical That gets me back to that back testing earlier With back testing, yeah, it looks cool but did you feel the pain when you went through that 40% drawdown like what did it feel like when you were on that slippery slope of two left feet? Completely different from the back test I think there's really no substitute for experience That doesn't mean you can't get lucky

    You can get lucky and get hit paydirt, but I wouldn't count on that HARI KRISHNAN: There's been an elephant in the room recently, which is, whether it's the short term financing markets, the repo markets or whatever, or anything to do with a short term credit facility in the markets, that seems that it has the potential to upset all strategies that rely on leverage How would you address that issue as someone who is nearly purely systematic? Is that something that you have to factor in? Do you just simply discount, lower your leverage a bit below where you think it should be? Do you have a dynamic way to account for it over time? Do you look at spreads? Do you look at repo spreads and things like that? VASANT DHAR: Not in futures markets where your leverage is inherent and more than you need The futures markets were actually trying to dap down the leverage because you can lever up to the hilt and really get into trouble In those markets, you're really levering down to your desired level of volatility and funding costs, they tend to rise more in the form of exchanges imposing, let's say, higher margins of stuff like that

    That's where your costs can increase, but that tends to be relatively rare It's a silver market goes berserk and the [indiscernible] limits or increase margins It's those– HARI KRISHNAN: The basic argument is that the repo issues or whatever that occurred in sometime are more impacting the cash bond market than the markets that a CTA-type structure would trade? VASANT DHAR: That's right HARI KRISHNAN: Got it Well, I guess an important question for a lot of viewers or participants like myself is, how's machine learning been oversold? If so, where? How can someone like you step in and help to educate people who wants to know more about the space? VASANT DHAR: Yeah, great question

    I think it has been oversold I think some of it is just this effect from the fact that it works in other areas, it's making its way into navigation, into language, into search Machine learning become an important part of our lives People just assume that let's do it in finance as well It's harder to do in finance, because it's a noisier problem

    It's not as easy to apply it to finance as it is to, let's say, a domain that has a lot of structure in it In that sense, I think it's been oversold It's not like you just put in data and magic appears at the other end There's a lot of care that has to go into how you formulate the problem, how you create the data, whether you have a process in place HARI KRISHNAN: Wasn't one of the early validations of machine learning or AI in general, just the human eye

    In other words, you get fuzzy images from telescopes and so on and then you could run some algo on them, which would see a huge number of images and then make sense of what this noisy image should look like Then it would clear it up and the human eye could say, oh, that's a real license plate I can read the number, whereas it was completely fuzzy before Doesn't that raise the issue that outliers, say if a camera takes a picture, and it's a bit fuzzy here and there, the outliers should be smoothed out or removed? Whereas in financial markets, those outliers are essential because they drive price action over the long– or they drive compounded returns more to the point over the long term How do you– do you think that part of it has been oversold? You think there's too much smoothing going on and not enough feature extraction? VASANT DHAR: I don't know whether I would state it like that, or at that level of detail

    I think that's just one piece of the problem HARI KRISHNAN: I've extracted too many features VASANT DHAR: I think it's an important part of the problem, this aspect of how do you deal with outliers in let's say vision versus finance and it and that's actually a whole conversation in itself that we could have That's a very meaty area I think it's that and lots of other things, the lack of structure in the problem that just make finance infinitely more challenging than, let's say, applying it to vision where there's so much structure to the problem, the stationarity and an unambiguous relationship between inputs and outputs, like however complex they may be, at the end of the day, people can say, well, that's a cat and that's a dog and that's a nine unless that nine is so fuzzified that even humans can't tell, in which case the machine can't tell

    For the training data where it's unambiguous, does an unambiguous mapping between inputs and outputs, that's a great problem for machine learning Whereas in finance, there isn't that unambiguous mapping You can have the same x with two different y's, by the same x, I mean the same market conditions as you specified them but the outcomes are completely different That's what noise really means in finance is the same market state but the outcomes are different Whereas in vision, it's a different problem

    If you take something that's worked in vision, it's all right, then applied in finance, it's not obvious that those ideas really carry over may actually get you in trouble You may be overly optimistic about what you can achieve HARI KRISHNAN: Well, it's been wonderful having this sound on the air I was looking forward to this and I'll leave it to him to sum up VASANT DHAR: It's been a pleasure

    I really enjoyed it Great set of questions and thank you

    Source: Youtube

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